Systems and methods for monitoring neural activity

ABSTRACT

A method of monitoring neural activity responsive to a stimulus in a brain, the method comprising: applying the stimulus to one or more of at least one electrode implanted in a target neural structure of the brain; detecting a resonant response from the target neural structure evoked by the stimulus at one or more of the at least one electrode in or near the target neural structure of the brain; and determining one or more waveform characteristics of the detected resonant response.

TECHNICAL FIELD

The present disclosure relates to deep brain stimulation (DBS) and, inparticular, methods and systems of monitoring neural activity responsiveto DBS.

BACKGROUND

Deep brain stimulation (DBS) is an established therapy for movementdisorders as well as other neurological disorders, including epilepsy,obsessive compulsive disorder, and depression. DBS is typicallyadministered to patients whose symptoms cannot be adequately controlledby medication alone. DBS involves surgically implanting electrodes in ornear to specific neural structures of the brain, typically in thesubthalamic nucleus (STN), the globus pallidus interna (GPi), and/or thethalamus. Electrodes are connected to a neurostimulator usuallyimplanted within the body and configured to deliver electrical pulsesinto target areas. It is believed that this electrical stimulationdisrupts abnormal brain activity causally linked to a patient'ssymptoms. Stimulation parameters can be adjusted using a controllerexternal to the body, remotely connected to the neurostimulator.

Whilst established DBS technology has proven to be effective inalleviating movement disorder symptoms, there are several limitations tostate of the art devices. In particular, established techniques forintraoperative testing of DBS electrodes to ensure correct positioningin the brain, such as x-ray imaging, microelectrode recordings, andclinical assessment can be inaccurate. Consequently, electrodes areoften implanted in suboptimal locations, resulting in diminishedtherapeutic outcomes and unwanted side-effects. After implantation, DBSdevices require manual adjustment by a clinician. This typicallyinvolves the clinician adjusting parameters of the stimulus based on alargely subjective assessment of immediate or short-term improvement ofthe patient's symptoms. Since therapeutic effects can be slow to emergeand because the DBS parameter space is large, the task of finding apreferred set of parameters is time- and cost-inefficient, and can leadto suboptimal therapeutic outcomes. In addition, the constant,non-varying application of electrical stimulation using conventional DBScan also lead to suboptimal therapeutic outcomes, including unwantedside effects, as well as reduced battery life of DBS stimulators.

SUMMARY

According to a first aspect of the disclosure, there is provided amethod of monitoring neural activity responsive to a stimulus in abrain, the method comprising: applying the stimulus to one or more of atleast one electrode implanted in the brain; detecting a resonantresponse from the target neural structure evoked by the stimulus at oneor more of the at least one electrode in or near a target neuralstructure of the brain; and determining one or more waveformcharacteristics of the detected resonant response.

The one or more waveform characteristics may be determined based on atleast part of a second or subsequent cycle in the detected resonantresponse.

The one or more waveform characteristics may comprise one or more of thefollowing: a) a frequency of the resonant response; b) a temporalenvelope of the resonant response; c) an amplitude of the resonantresponse; d) a fine structure of the resonant response; e) a rate ofdecay of the resonant response; f) a delay between the onset of thestimulus and the onset of a temporal feature of the resonant response.

The stimulus may comprise a plurality of pulses.

The step of determining the one or more waveform characteristics maycomprise comparing a first characteristic over two or more cycles of thedetected resonant response. The step of determining the one or morewaveform characteristics may comprise determining a change of the firstcharacteristic over the two or more cycles. The step of determining theone or more waveform characteristics may comprise determining a rate ofchange of the first characteristic over the two or more cycles.

The resonant response may comprise a plurality of resonant components.One or more of the plurality of resonant components from a neuralstructure different from the target neural structure.

The method may further comprise adjusting the location of one or more ofthe at least one electrodes based on the one or more determined waveformcharacteristics.

The method may further comprise adapting the stimulus based on the oneor more determined waveform characteristics of the resonant response.The adapting may comprise adjusting one or more of the frequency,amplitude, pulse-width, electrode configuration, or morphology of thestimulus.

The method may further comprise correlating the detected resonantresponse with a template resonant response; and adapting the stimulusbased on the correlation. The method may further comprise correlatingthe one or more determined waveform characteristics with one or morepredetermined threshold values; and adapting the stimulus based on thecorrelation.

The stimulus may be non-therapeutic or therapeutic.

The stimulus may comprise a patterned signal comprising a plurality ofbursts separated by a first time period, each burst comprising aplurality of pulses separated by a second time period, wherein the firsttime period is greater than the second time period and wherein thedetecting is performed during one or more of the first time periods. Thefirst time period may be greater than or equal to the second timeperiod. The plurality of pulses within at least one of the bursts mayhave different amplitudes. The different amplitudes may be selected toproduce a ramp in amplitude of sequential pulses in the at least one ofthe bursts. The final pulse in each of the plurality of bursts may besubstantially identical.

The method may further comprise: estimating a patient state of a patientbased on the determined one or more waveform characteristics. In whichcase, the method may further comprise diagnosing the patient based onthe estimate of the patient's state and/or generating one or more alertsassociated with the estimated patient state; and outputting the one ormore alerts.

The method may further comprise applying a second stimulus to a targetneural structure in the brain; detecting a second resonant response fromthe target neural structure evoked by the second stimulus at one or moreof the at least one electrode implanted in or near the target neuralstructure; determining one or more second waveform characteristics ofthe detected second resonant response.

The method may further comprise: estimating a degree of progression of adisease associated with the patient based on the one or more firstwaveform characteristics and the one or more second waveformcharacteristics.

The method may further comprise: determining the effect of a therapyprovided to the patient based on the one or more first waveformcharacteristics and the one or more second waveform characteristics. Thetherapy may be medication or deep brain stimulation.

The at least one electrode may comprise two or more electrodes locatedwithin different neural structures in the brain. The at least oneelectrode may comprise two or more electrodes located within differenthemispheres of the brain.

The method may further comprising: determining whether one or more ofthe at least one electrode is positioned in the target neural networkbased on the detected resonant response. The method may further comprisemoving one or more of the first electrode and the second electrode basedon the detected resonant response.

The steps of applying the stimulus, detecting a resonant response anddetermining one or more waveform characteristics of the detectedresonant response, may be repeated one or more times so that a series ofresonant responses are detected, each in response to application of aseparate signal. These steps may be repeated until it is determined thatone or more of the at least one electrode is positioned in the targetneural structure.

The method may further comprise comparing a common waveformcharacteristic between two or more detected resonant responses.

The method may further comprise comparing a degree of change of a commoncharacteristic between two or more detected resonant responses.

The method may further comprise determining a rate of change of a commoncharacteristic between two or more detected resonant responses.

The method may further comprise selecting one or more of the at leastone electrode to use for therapeutic stimulation of the target neuralstructure based on the one or more waveform characteristics; andapplying a therapeutic stimulus to the target neural structure via theselected one or more of the at least one electrode.

The method may further comprise: inserting the at least one electrodeinto the brain along a predefined trajectory; wherein steps of applyingthe stimulus, detecting a resonant response and determining one or morewaveform characteristics of the detected resonant response are repeatedwhile the at least one electrode is being inserted to generate a profileof resonant responses with respect to the predefined trajectory and thetarget neural structure.

The profile of resonant responses may be used to determine a position ofthe one or more electrodes relative to the target neural structure.

The at least one electrode may comprises a plurality of electrodes, andthe steps of applying the stimulus, detecting a resonant response anddetermining one or more waveform characteristics of the detectedresonant response may be repeated using different combinations of the atleast one electrode to generate a profile of resonant responses.

The method may further comprise: selecting one or more of the at leastone electrode based on the profile of neural responses; and applying atherapeutic stimulus to the selected one or more of the at least oneelectrode. The selected one or more of the at least one electrode maycomprise a plurality of electrodes.

The one or more of the at least one electrode used to apply the stimulusmay comprise at least two electrodes. Equally, the one or more of the atleast one electrode used to detect the resonant response may comprise atleast two electrodes.

The neural target structure may be part of the cortico-basalganglia-thalamocortical circuit.

The neural target structure may be the subthalamic nucleus, globuspallidus interna, substantia nigra pars reticulata, pedunculopontinenucleus.

According to a second aspect of the disclosure, there is provided aneurostimulation system, comprising: a lead having at least oneelectrode adapted for implantation in or near a target neural structurein the brain; a signal generator selectively coupled to one or more ofthe at least one electrode and configured to generate a stimulus tostimulate the target neural structure; a measurement device selectivelycoupled to one or more of the at least one electrode and configured todetect a resonant response from the target neural structure evoked bythe stimulus; a processing unit coupled to the measurement device andconfigured to determine one or more waveform characteristics of thedetected resonant response.

The one or more waveform characteristics may be determined based on atleast part of a second or subsequent cycle in the detected resonantresponse.

The one or more waveform characteristics comprises one or more of thefollowing: a) a frequency of the resonant response; b) a temporalenvelope of the resonant response; c) an amplitude of the resonantresponse; d) a fine structure of the resonant response; e) a rate ofdecay of the resonant response; f) a delay between the onset of thestimulus and the onset of a temporal feature of the resonant response.

The stimulus may comprise a plurality of pulses.

In determining the one or more waveform characteristics, the processingunit may be configured to correlate a first characteristic over two ormore cycles of the detected resonant response.

In determining the one or more waveform characteristics, the processingunit may be configured to determine a degree of change of the firstcharacteristic over the two or more cycles.

In determining the one or more waveform characteristics, the processingunit may be configured to determine a rate of change of the firstcharacteristic over the two or more cycles.

The resonant response may be detected at a different one or moreelectrodes to the one or more electrodes at which the stimulus isapplied.

The resonant response may comprise a plurality of resonant components.

The processing unit may be coupled to the signal generator andconfigured to selectively control the output of the signal generator.

The processing unit may be configured to: control the signal generatorto adapt the stimulus based on the one or more determined waveformcharacteristics of the resonant response.

The processing unit may be further configured to: correlate the detectedresonant response with a template resonant response; and control thesignal generator to adapt the stimulus based on the correlation.

The processing unit may be configured to: correlate the one or moredetermined waveform characteristics with one or more predeterminedthreshold values; and control the signal generator to adapt the stimulusbased on the correlation.

The adapting may comprise adjusting one or more of the frequency,amplitude, pulse-width, electrode configuration, or morphology of thestimulus.

The stimulus may be non-therapeutic or therapeutic.

The stimulus may comprise a patterned signal comprising a plurality ofbursts separated by a first time period, each burst comprising aplurality of pulses separated by a second time period, wherein the firsttime period is greater than the second time period and wherein thedetecting is performed during one or more of the first time periods. Thefirst time period is greater than or equal to the second time period.The plurality of pulses within at least one of the bursts may havedifferent amplitudes.

The different amplitudes may be selected to produce a ramp in amplitudeof sequential pulses in the at least one of the bursts.

The final pulse in each of the plurality of bursts is preferablysubstantially identical.

The processing unit may be configured to: estimate a patient state of apatient based on the determined one or more waveform characteristics.

The processing unit may be configured to: diagnosing the patient basedon the estimate of the patient's state.

The processing unit may be configured to: generating one or more alertsassociated with the estimated patient state; and output the one or morealerts.

The processing unit may be configured to: estimate a degree ofprogression of a disease associated with the patient or an effect of atherapy provided to the patient based on the one or more waveformcharacteristics and one or more second waveform characteristics, the oneor more second waveform characteristics determined based on a secondresonant response detected after the resonant response.

The therapy may be medication or deep brain stimulation.

The system may further comprise: a second lead having at least onesecond electrode adapted for implantation in or near a second targetstructure in the brain; wherein the signal generator is selectivelycoupled to one or more of the at least one second electrode andconfigured to generate a stimulus to stimulate the second target neuralstructure; wherein the measurement device is selectively coupled to oneor more of the at least one second electrode and configured to detect aresonant response from the second target neural structure evoked by thestimulus; a processing unit coupled to the measurement device andconfigured to determine one or more waveform characteristics of thedetected resonant response from the second target neural structure.

The lead and the second lead may be located within or near to differentneural structures in the brain. The lead and the second lead may belocated within different hemispheres of the brain.

The processing unit may be configured to: determine whether one or moreof the at least one electrode is positioned in the target neural networkbased on the detected resonant response.

The signal generator, the measurement device and the processing unit aremay be configured to repeat the steps of applying the stimulus,detecting a resonant response and determining one or more waveformcharacteristics of the detected resonant response. In which case, thesteps of applying the stimulus, detecting a resonant response anddetermining one or more waveform characteristics of the detectedresonant response may be repeated until processing unit determines thatone or more of the at least one electrode is positioned in the targetneural structure.

The processing unit may be configured to control the signal generatorto: select one or more of the at least one electrode to use fortherapeutic stimulation of the target neural structure based on the oneor more waveform characteristics; and apply a therapeutic stimulus tothe target neural structure via the selected one or more of the at leastone electrode.

The steps of applying the stimulus, detecting a resonant response anddetermining one or more waveform characteristics of the detectedresonant response may be repeated while the at least one electrode isbeing inserted; and the processing unit may be further configured togenerate a profile of resonant responses with respect to the predefinedtrajectory and the target neural structure.

The processing unit may be configured to determine a position of the oneor more electrodes relative to the target neural structure based on theprofile of resonant responses.

The at least one electrode may comprise a plurality of electrodes. Inwhich case, the steps of applying the stimulus, detecting a resonantresponse and determining one or more waveform characteristics of thedetected resonant response may be repeated using different combinationsof the at least one electrode to generate a profile of resonantresponses.

The processing unit may be configured to: select one or more of the atleast one electrode based on the profile of neural responses; andcontrol the signal generator to applying a therapeutic stimulus to theselected one or more of the at least one electrode.

The selected one or more of the at least one electrode may comprise aplurality of electrodes.

The one or more of the at least one electrode used to apply the stimulusmay comprise at least two electrodes and/or wherein the one or more ofthe at least one electrode used to detect the resonant responsecomprises at least two electrodes.

The neural target structure may be part of the cortico-basalganglia-thalamocortical circuit.

The neural target structure may be the subthalamic nucleus, globuspallidus interna, substantia nigra pars reticulata, pedunculopontinenucleus.

According to a third aspect of the disclosure, there is provided amethod of monitoring neural activity in a brain responsive to anstimulus, the method comprising: a. applying the stimulus to a targetneural structure in the brain; and

b. detecting a neural response evoked by the stimulus at an electrodeimplanted in or near the target neural structure, wherein the stimuluscomprises a patterned signal comprising a plurality of bursts separatedby a first time period, each burst comprising a plurality of pulsesseparated by a second time period, wherein the first time period isgreater than the second time period and wherein the detecting isperformed during one or more of the first time periods.

The first time period is preferably greater than or equal to the secondtime period.

The stimulus is preferably biphasic. The plurality of pulses within aburst may have different amplitudes. The different amplitudes may beselected to produce a ramp. The final pulse in each of the plurality ofbursts may be substantially identical. The stimulus may be patterned tobe non-therapeutic or therapeutic.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the present disclosure will now be described by way ofnon-limiting examples with reference to the drawings, in which:

FIG. 1 is a graph illustrating resonance from a neural structureresponsive to a deep brain stimulation (DBS) signal;

FIG. 2 is a graph illustrating resonance from a neural structureresponsive to a patterned DBS signal;

FIG. 3 is a graph illustrating evoked resonance responsive to 10consecutive pulses of a DBS signal;

FIG. 4 is a graph illustrating the range and variance of peak amplitudeof a resonant response to continuous and patterned DBS;

FIG. 5A is a graphical illustration showing neural resonance evoked by acontinuous non-therapeutic patterned DBS signal;

FIG. 5B is a graphical illustration showing neural resonance evoked by acontinuous therapeutic patterned DBS signal;

FIG. 5C is a graphical illustration showing neural resonance after atransition from a continuous therapeutic DBS signal to a non-therapeuticDBS signal;

FIG. 5D is a graph illustrating the estimated frequency of evokedresonance responsive to a non-therapeutic DBS signal;

FIG. 5E is a graph illustrating the estimated frequency of evokedresonance responsive to a therapeutic DBS signal;

FIG. 5F is a graph illustrating the estimated frequency of evokedresonance responsive to a transition between a therapeutic DBS signaland a non-therapeutic DBS signal;

FIG. 6A is a graphical illustration showing neural resonance evoked by acontinuous non-therapeutic patterned DBS signal;

FIG. 6B is a graphical illustration showing neural resonance evoked by acontinuous therapeutic patterned DBS signal;

FIG. 6C is a graphical illustration showing neural resonance after atransition from a continuous therapeutic DBS signal to a non-therapeuticDBS signal;

FIG. 6D is a graph illustrating the estimated frequency of evokedresonance responsive to a non-therapeutic DBS signal;

FIG. 6E is a graph illustrating the estimated frequency of evokedresonance responsive to a therapeutic DBS signal;

FIG. 6F is a graph illustrating the estimated frequency of evokedresonance responsive to a transition between a therapeutic DBS signaland a non-therapeutic DBS signal;

FIG. 7A is a graph illustrating evoked resonances beginning to divergeinto two peaks in response to patterned DBS with an amplitude of 1.5 mA;

FIG. 7B is a graph illustrating evoked resonances diverging into twopeaks in response to patterned DBS with an amplitude of 2.25 mA;

FIG. 7C is a graph illustrating two separate evoked resonant peaks inresponse to patterned DBS with an amplitude of 3.375 mA;

FIG. 8 is a schematic illustration of an electrode lead tip forimplantation in a brain;

FIG. 9 is a schematic illustration of an electrode lead implanted in thesubthalamic nucleus of a brain;

FIG. 10 is a schematic illustration of a system for administering DBS;

FIG. 11 is a flow diagram illustrating a process for locating a DBSelectrode in the brain;

FIG. 12 is a flow diagram illustrating a process for monitoring andprocessing resonant responses at multiple electrodes in response tostimulation at multiple electrodes;

FIG. 13 is a graphical illustration of resonant responses measured atdifferent electrodes implanted in a brain responsive to a stimulationsignal in accordance with the process shown in FIG. 12;

FIG. 14 is a graphical illustration of resonant responses measured atdifferent electrodes implanted in a brain responsive to stimulationsignals applied at different electrodes in accordance with the processshown in FIG. 12;

FIG. 15 is a flow diagram illustrating a process for determiningparameters for a DBS stimulation signal based on medicating the patient;

FIG. 16 is a flow diagram illustrating a process for generating astimulation signal with closed-loop feedback based on evoked resonanceat a target neural structure;

FIG. 17 is a flow diagram illustrating another process for generating astimulation signal with closed-loop feedback based on evoked resonanceat a target neural structure;

FIG. 18 graphically illustrates switching between periods of therapeuticand non-therapeutic stimulation relative to a resonant activity featureof an evoked response in accordance with the process of FIG. 17;

FIG. 19A illustrates a patterned stimulation signal according to anembodiment of the present disclosure;

FIG. 19B illustrates another patterned stimulation signal according toan embodiment of the present disclosure

FIG. 20 illustrates the results of movement function tests for patientsreceiving DBS;

FIGS. 21A to 21G are graphical illustration showing neural resonanceevoked by a continuous therapeutic patterned DBS signal;

FIG. 22 is a three-dimensional reconstructions of an electrode arrayimplanted in a STN (smaller mass) and subantia nigra. (larger mass) of apatient;

FIGS. 23A to 23C are merged MRI and CT scans of a patient's brainshowing the positioning of an electrode array;

FIG. 24 graphically illustrates the variation in ERNA amplitude relativeto electrode position;

FIG. 25A graphically illustrates ERNA frequency vs DBS amplitude for 19brain hemispheres;

FIG. 25B graphically illustrates ERNA amplitude vs DBS amplitude for 19brain hemispheres;

FIG. 25C graphically illustrates Relative beta(RMS_(13-30 Hz)/RMS_(4-45 Hz)) VS DBS amplitude for 19 brainhemispheres;

FIG. 25D graphically illustrates Relative beta correlated with ERNAfrequency (ρ=0.601, p<0.001) for 19 brain hemispheres;

FIG. 26A graphically illustrates ERNA frequency washout over consecutive15 s periods post-DBS and in the last 15 s pre-DBS for 19 brainhemispheres;

FIG. 26B graphically illustrates ERNA amplitude washout for 19 brainhemispheres;

FIG. 26C graphically illustrates Relative beta washout for 19 brainhemispheres; and

FIG. 26D graphically illustrates Washout in the 200-400 Hz HFO band for19 brain hemispheres.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present disclosure relate to improvements inneuro-stimulation in the brain. DBS devices typically apply a constantamplitude stimulus to a target area of the brain at a constant frequencyof 130 Hz. The inventors have determined not only that application ofsuch a stimulus evokes a neural response from the target area of thebrain, but that the neural response comprises a resonant component whichhas not previously been recognised. Continuous DBS at conventionalfrequencies does not allow a long enough time window to observe theresonant activity. However, by monitoring the neural response afterstimulation has ceased (by patterning the stimulation signal orotherwise), the resonant activity can be monitored. In addition, theinventors have realised that embodiments of the present invention haveapplications both for reducing the physical effects associated withmotor diseases, and also the detrimental effects of other neurologicalconditions, neuropsychiatric disorders, sensory disorders, and pain.

In addition to the above, the inventors have also realised that neuronaloscillations, as reflected in local field potentials measured, forexample, via implanted electrodes, by EEG, or by MEG, are also affectedboth by DBS and certain medications used to treat movement disorders. Inparticular, high frequency oscillations (HFOs) in the range of 200 to400 Hz, measured in local field potentials by DBS electrodes implantedin the subthalamic nucleus (STN) of the brain, have been found to bemodulated both by DBS and with the use of medication, such as levodopa.This realisation has led the inventors to develop novel techniques ofselecting optimal DBS treatment parameters based on measured HFOmodulation. FIG. 1 graphically illustrates a response from a neuralcircuit stimulated by a 130 Hz signal delivered from a neurostimulatorvia an electrode lead, such as the 3387 electrode lead manufactured byMedtronic®, implanted in the subthalamic nucleus (STN) of a Parkinson'sdisease (PD) patient. Each response to a stimulus pulse comprises anevoked compound action potential (ECAP) component together with acomponent of evoked resonant neural activity (ERNA) occurring after theECAP. The ECAP typically occurs within 1-2 milliseconds of the stimuluspulse. The graph shows the response to the last three consecutive pulsesof a 60 second period of continuous stimulation followed by a period ofno stimulation. It can be seen that the evoked resonant response to eachof the first two stimulus pulses shown in FIG. 1 is cut short by theonset of the next stimulus pulse, such that only a single secondary peakis detected. However, the evoked resonant response to the third (andfinal) pulse is able to resonate for longer and so can be clearly seenin the form of a decaying oscillation with at least seven peaks for apost-stimulus period of about 30 milliseconds.

As mentioned above, it is known for clinicians to control and adjust DBSparameters to elicit therapeutic effects in a patient. The inventorshave realised that by controlling the DBS parameters in certain ways, anon-therapeutic stimulus can be administered which evokes a resonantneural response (ERNA) in a patient without having any therapeuticimpact or causing undesirable side effects. Such non-therapeutic stimulican be used to reliably measure ERNA without causing sustained changesto the resonant neural circuit or the patient's symptomatic state.Non-therapeutic stimulation is preferably achieved by administering astimulus comprising a short burst of pulses followed by a period of nostimulation, and the ERNA is measured during this period of nostimulation. By doing so, the total charge or energy provided to thepatient is below a therapeutic threshold, and the measured ERNA providesinformation concerning the patient's natural state (without therapy). Inan alternative embodiment, the overall charge or energy provided to thepatient may be reduced by reducing the amplitude of the stimulationsignal below a therapeutic threshold. However, doing so may also reducethe amplitude of peaks in the ERNA making it more difficult to observe.

In addition to the above, the inventors have determined that patternedstimulation can be used to monitor and analyse evoked resonant neuralactivity during therapeutic stimulation of a patient. By patterning thestimulation signal, therapeutic stimulation can be maintained whilstproviding time windows in which to monitor resonant responses past thatof the first resonant peak or more preferably past two or more resonantpeaks.

FIG. 2 graphically illustrates an example therapeutic patterned DBSstimulus 20 and the associated evoked resonant response according to anembodiment of the present disclosure. The patterned stimulus 20 is shownabove the graph to illustrate the correlation between stimulus andresponse. In the patterned stimulus, a single pulse has been omittedfrom an otherwise continuous 130 Hz pulse train. The pulse traintherefore includes a plurality of bursts of pulses of continuousstimulation, each burst separated by a first time period t₁, each of theplurality of pulses separated by a second time period t₂. Continuationof the stimulus before and after omission of a pulse (or more than onepulse) maintains the therapeutic nature of the DBS, whilst the omissionof a pulse allows for resonance of the ERNA to be monitored over several(3 in this example) resonant cycles before the next stimulation pulseinterrupts this resonance.

In summary, by patterning non-therapeutic and therapeutic stimuli, anevoked response can be monitored over a longer period of time than withconventional non-patterned stimulation. Accordingly, stimuli arepreferably applied in bursts of multiple pulses, each burst separated bya first time period t₁ of no stimulation, each pulse separated by asecond time period t₂. For example, a stimulus signal may comprise aseries of 10-pulse bursts at 130 Hz. To increase repeatability ofresults, the multi-pulse burst may be repeated after a predeterminedperiod of no stimulation. For example, the multi-pulse burst may berepeated each second. The duration of the first time period t₁ isgreater than that of the second time period t₂. The ratio between theduration of the burst and the duration between bursts may be chosen soas to ensure that relevant properties of the ERNA can be monitoredeasily and efficiently. In some embodiments, the duration of each burstis chosen to be between 1% and 20% of the duration of no stimulationbetween bursts.

In other embodiments, the duration of each burst may be chosen tominimise the effects of stimulation on the measured ERNA or toaccentuate particular features of the measured ERNA. FIG. 3 graphicallyillustrates how the application of 10 pulses at 130 Hz can affect ERNA.The response to the first pulse has a broad, low amplitude first peak.The first peak becomes larger and sharper for subsequent pulses, whilstalso shifting to an earlier time. In some embodiments, the optimumnumber of pulses comprised in a burst may be chosen to maximise theamplitude of the resonance, whilst minimizing the time shift of a peakin ERNA across the burst (e.g, the fourth pulse). In other embodiments,the rate of change in ERNA features (e.g. amplitude, onset delay) acrossconsecutive pulses in a burst may be used as a defining characteristic.For example the rate of change across a burst may be used to determineelectrode position, optimum parameters, patient state, etc. and/or as aclosed loop control signal.

The use of bursts (e.g. 10 pulses) stimulation provides high amplitudeevoked neural responses, making them easier to measure than responses tomore continuous DBS. FIG. 4 graphically illustrates the range andvariance of first peak amplitude of ERNA responsive to more continuousDBS where one pulse is skipped every second (left) and burst DBS (right)(10 pulses only per second). It can be seen that the average peakamplitude of ERNA responsive to burst DBS is around 310 μV whereas theaverage peak amplitude of ERNA responsive to more continuous DBS isaround 140 μV. Further, by using burst stimulation, the evoked resonantresponse over several oscillatory cycles (20 milliseconds or more) canbe monitored.

By analysing characteristics of the ERNA, the inventors have determinedthat waveform characteristics of the ERNA (natural frequency, dampingfactor, envelope, fine structure, onset delay, rate of change, etc.) aredependent on various physiological conditions of the patient. Forexample, it has been found that therapeutic DBS decreases the frequencyof resonance of the target neural circuit.

Changes in ERNA Related to DBS Stimulation

FIGS. 5A, 5B, and 5C illustrate the variation of frequency of the ERNAduring non-therapeutic stimulation (FIG. 5A), therapeutic stimulation(FIG. 5B), and the ERNA after a transition of stimulation fromtherapeutic stimulation to non-therapeutic stimulation (FIG. 5C).Resonant frequency of the ERNA was measured by calculating the inverseof the time delay between the maxima of two peaks of the ERNA. In otherembodiments, the resonant frequency may be calculated as an inverse ofthe average time delay between maxima of all detected peaks of the ERNA.In further embodiments, the resonant frequency may be calculated byfitting a damped oscillator model to the resonant activity andextracting the natural frequency or by performing spectral analyses(e.g. Fourier transform, wavelet transform). Other techniques forfrequency estimation, such as estimating the time between zero-crossingsin the waveform or using other features of the waveform may also be usedfor this purpose.

In the example shown, a patterned stimulus was administered to thepatient in the same manner as described with reference to FIGS. 1 and 2.FIGS. 5A and 5B show the responses to a patterned non-therapeutic andtherapeutic DBS stimulation, respectively. In this example,non-therapeutic stimulation consisted of bursts of 10 pulses deliveredat a frequency of 130 Hz over a 1-second time period with the remaining120 pulses (which would be present during continuous stimulation)skipped. The typical observable window of the response (duringcontinuous (non-patterned) DBS) is denoted by the horizontal dottedline. It can be seen that with patterned non-therapeutic stimulation,the amplitude and frequency of the ERNA remain relatively constantindicating that the stimulus did not strongly affect the resonant stateof the target neural structure over time. Further, two resonant peaks ofthe ERNA (represented in black) can be seen in the typical observablewindow for non-patterned stimulation. FIG. 5B then shows the responsesto therapeutic patterned DBS stimulation at 3.375 mA where 129 pulsesare delivered per second at a rate of 130 Hz, with the remaining 1 pulseskipped.

The therapeutic signal causes the frequency of the ERNA to reduce, inturn potentially causing the second resonant peak of the ERNA to moveoutside the typical observable window for continuous (non-patterned)stimulation. However by patterning the stimulation by skipping one ormore pulses, it is possible to continue to measure the resonantproperties of the ERNA, along with subsequent peaks during the period inwhich a stimulation pulse is omitted. Additionally, it can be seen thatthe amplitude of the third and fourth resonant peaks are increased incomparison to the non-therapeutic responses.

Alternative methods of patterning the stimulation, rather than merelyomitting pulses in a periodic pulse train, may improve the monitoring ofERNA. For example, conventional, therapeutic stimulation (e.g. at afrequency of 130 Hz) may be interleaved with bursts of stimulationhaving a lower frequency (e.g. 90 Hz). The frequency of theseinterleaving bursts is preferably low enough to allow for multiple ERNApeaks to be observed. Equally, the frequency of these interleavingbursts is preferably high enough to be within the therapeutic frequencyrange for DBS. The transition between frequency may be abrupt or,alternatively the change in frequency may be gradual. Applying ramps tothe frequency of the pulses to avoid an abrupt step change in frequencymay be advantageous.

Additionally or alternatively to adjusting the frequency of the appliedstimulus, the amplitude of pulses may be modulated over time. This mayinclude applying a ramp to increase the pulse amplitudes over severalpulses within a burst and/or a ramp to decrease the pulse amplitudesover several pulses within a burst. To enhance the monitoring of ERNA itmay be advantageous to apply a fixed amplitude to the pulses precedingthe observation window, and if this amplitude differs from that appliedat other times (e.g. to maximise therapeutic benefit), then applyingramps to the amplitude of the pulses to avoid an abrupt step change inamplitude may be advantageous.

FIG. 5C then shows the responses after switching back to thenon-therapeutic patterned stimulation. In this case the therapeuticeffect of the patterned therapeutic stimulus ‘washes out’ and the ERNAreturns to its baseline state. It can be seen that the first peak ofresonant activity across all conditions (typically all that can bemeasured using conventional continuous DBS) does not vary greatly withtherapeutic DBS. However, characteristics of subsequent parts of theERNA waveform, made measurable by patterning the stimulation, exhibitmuch larger changes in frequency and amplitude. Monitoring of theresponse over a longer period therefore enables information concerningfrequency, amplitude, envelope, and fine structure of the time-varyingoscillation to be analysed.

This effect is further illustrated by FIGS. 5D, 5E, and 5F. FIG. 5Dshows the resonant frequency of the ERNA during periods ofnon-therapeutic stimulation to be around 400-450 Hz. Clinicallyeffective stimulation (stimulation operable to actively reduce apatient's disease symptoms) reduces the frequency of the ERNA to around300-350 Hz as shown in FIG. 5E. FIG. 5F illustrates the transition ofresonant frequency from 300-350 Hz back to around 400-450 Hz aftertherapeutic stimulation has been replaced with non-therapeuticstimulation.

FIGS. 6A, 6B, and 6C illustrate another example from a different patientof the variation of the ERNA during non-therapeutic patternedstimulation (FIG. 6A), therapeutic patterned stimulation (FIG. 6B) andthe ERNA after a transition from therapeutic stimulation tonon-therapeutic stimulation (FIG. 6C). In this example, patternedstimuli were administered to the patient in the same manner as describedwith reference to FIGS. 5A to 5E. As with the previous example, it canbe seen that the initial non-therapeutic stimulation (FIG. 6A) does notcause noticeable changes to the ERNA and that the therapeuticstimulation (FIG. 6B) causes a reduction in the frequency of theresonance, which returns to baseline levels after the stimuli istransitioned back to non-therapeutic patterned stimulation (FIG. 6C).However, in this example, the change in resonant frequency withtherapeutic stimulation is accompanied by an increase in the delaybetween each stimulus pulse and the onset of the resonance. Thisincrease in onset delay shifts the second resonant peak such that itoccurs outside the typical observable window for conventional(non-patterned) DBS. By patterning the stimulation, the measurementwindow is made long enough to observe three resonant peaks, allowingERNA to be characterised. Furthermore, contrary to the previous example,the amplitude of the resonance is decreased by therapeutic stimulation.

FIGS. 6D, 6E, and 6F further illustrate the reduction in resonantfrequency with therapeutic stimulation in this example. Resonantfrequency was estimated by calculating the inverse of the time delaybetween the maxima of two peaks of the ERNA. In FIG. 6D, the frequencyof the ERNA measured using non-therapeutic patterned stimulation can beseen to be about 350 Hz. The application of therapeutic patternedstimulation in FIG. 6E causes the frequency to decrease to around 250Hz. The frequency can be seen to be returning to its baseline level inFIG. 6F after transitioning back to non-therapeutic patternedstimulation.

ERNA Comprising Multiple Resonances

The inventors have determined not only that evoked neural responses toapplied stimuli exhibit resonant activity, but that in some instancesevoked activity comprises multiple resonances. FIGS. 7A, 7B and 7Cillustrates ERNA in response to continuous DBS at 1.5 mA, 2.25 mA and3.375 mA respectively. At 1.5 mA the resonant ERNA starts as a singlepeak, which can be seen to begin to diverge slightly into two peaks. At2.25 mA, the dominance switches to the later of the two peaks. However,the earlier peak, which was dominant at 1.5 mA, continues at a loweramplitude. At 3.375 mA, two peaks are present, with the later peakdominating. It is thought that these multiple resonant peaks correspondto activity in different neural circuits. The relative amplitude betweenthese resonant responses (or other features, such as temporal orspectral properties) may be an indicator of therapeutic state.

ERNA Measurements from Chronically Implanted Electrodes

FIGS. 20 to 23C provide further evidence of the positive effects of DBSon patient symptoms and related changes in ERNA. The data shown in thesefigures was collected from a patient with Parkinson's disease implantedwith an electrode array. The electrode array was implanted chronically,and measurements of ERNA and movement state were made several monthsafter implantation. By measuring ERNA and movement state at this time,it could be presumed that the electrode array and its neural environmentwere stable. Accordingly, the confirmed relationship between ERNA andmovement state is more representative of the long-term condition of apatient than those relationships measured during acute intraoperativeprocedures, or studies conducted within a few days of electrodeinsertion. It has been found that findings from such short-term studiesmay be confounded by a ‘stun effect’ that is characterised by temporaryalleviation of motor deficits in Parkinson's disease presumably relatedto the surgical implantation procedure rather than application oftherapeutic DBS itself.

ERNA measurements were collected in a similar manner to that describedabove with reference to FIGS. 5A to 6F. Measurements of the patient'smovement functions were also collected. These included estimates ofmuscle rigidity, speed of finger tapping, and facility of opening andclosing the hand.

DBS at different stimulation amplitudes was applied to the implantedelectrode array in a similar manner to that described above. Stimulationamplitudes included zero, 0.667 mA, 1 mA, 1.5 mA, 2.25 mA and 3.375 mA.Non-therapeutic burst stimuli were also provided before and after theperiods of conventional DBS.

FIG. 20 graphically illustrates the results of the movement functiontests. The observations of rigidity, finger tapping and opening/closingof the patient's hand are shown separately, with the average of thosemeasures shown in bold. The observations of patient impairment werescored from 0 (zero) to 4 where 4 indicated the most impairment and zeroindicated the least (or no) movement. This scale is provided on thevertical axis of FIG. 20.

It is evident from FIG. 20 that DBS improved all movement scores, withthe average score showing a marked benefit, particularly at the highestlevels of DBS (2.25 mA and 3.375 mA). Patient movement began to returnto pre-DBS conditions during the final burst washout period, duringwhich stimulation parameters were selected so as to produce notherapeutic effect.

FIGS. 21A, 21B, 21C, 21D and 21E show data extracted from ERNA waveformsrecorded during patterned therapeutic stimulation with amplitudes of0.667 mA, 1 mA, 1.5 mA, 2.25 mA and 3.375 mA respectively. Inparticular, this data is representative of ERNA waveforms recorded inresponse to the final stimulation pulse preceding a period of nostimulation (skipped pulse) as described above (with reference to FIGS.1 and 2). Patterned stimulation was applied over several minutes(represented on the horizontal axis). ERNA waveform peaks arerepresented by darker points. ERNA waveform troughs are represented bylighter points. FIGS. 21F and 21G show ERNA before and after continuousDBS, during periods of burst (non-therapeutic) stimulation.

Taken together, the results shown in FIGS. 20 and 21A-21G show thatthere is a clear correlation between ERNA and the effectiveness of DBSon alleviating movement disorders. Accordingly, these results providefurther evidence that characteristics of ERNA may be used to control DBSparameter settings to optimise therapy for individual patients.

For example, if the measured ERNA waveform shows a peak at around 7 msand an adjacent peak just below 11 ms (as shown in FIGS. 21D and 21E)then it can be assumed that DBS is effective at reducing symptoms. Peaksat these times occurred only when stimulation was applied at 2.25 mA and3.375 mA, corresponding to DBS conditions where the patient's movementfunctions were less impaired (see FIG. 20). In this case, the DBSwaveform can be adjusted to reduce the energy being applied to thebrain. Such adjustment may comprise adjusting one or more of frequency,amplitude, pulse-width, net charge, or morphology of the stimulus. Suchadjustment can minimise battery usage, reduce adverse side-effectsassociated with DBS, and improve safety of chronic stimulation.Otherwise, if the measured ERNA waveform shows the correspondingadjacent peaks below 7 ms and 11 ms respectively, then it can be assumedthat DBS is not effective and the amplitude of DBS should be increased.For example, FIG. 21C shows adjacent peaks at approximately 6 ms and 9ms in response to a stimulation amplitude of 1.5 mA, which was lesseffective at alleviating motor dysfunction as evidenced by FIG. 20.

In another example, if there are two adjacent peaks present in the ERNAwaveform and they are separated in time by more than approximately 3.5ms, as is the case in FIGS. 21D and 21E, then it can be assumed the DBSis effective. If, on the other hand, the time difference betweencorresponding adjacent peaks is less than 3.5 ms, as is the case inFIGS. 21A and 21B, then it can be assumed that DBS is ineffective. TheDBS waveform can then be controlled to maintain DBS parameters (e.g.frequency, amplitude, pulse-width, net charge, or morphology) at levelswhich provides effective symptom relief whilst preferably alsominimizing battery usage, reduce adverse side-effects associated withDBS, and improve safety of chronic stimulation.

Whilst the results shown in FIGS. 20 and 21A-21G show a relationshipbetween measured ERNA and DBS amplitude in particular, it will beappreciated that a relationship exists between measured ERNA and anyother parameter of the DBS waveform, including but not limited tofrequency, amplitude, pulse-width, overall net charge, and morphology.

It will also be appreciated that delays in an initial peak in an ERNAwaveform and delays between ERNA peaks are likely to bepatient-specific. Advantageously, any reliance on such data forcontrolling DBS stimulation will be based on an initial characterisationof ERNA waveforms and movement impairment associated with each patientin order to generate a regime for stimulation control (e.g. closed loopcontrol).

As has been mentioned briefly above with reference to FIGS. 20 and21A-21G, the identification of a correlation between changes in resonantbehaviour of stimulated neural circuits and a patient's disease symptomspresent several opportunities to improve aspects of DBS therapy,including but not limited to techniques for initial implantation andsubsequent repositioning of DBS electrodes, together with techniques forsetting parameters of DBS stimulation and using feedback to adjust DBSparameters in real time whilst DBS therapy is underway.

A number of practical applications of the above described evokedresonant neural activity will now be discussed with reference to severalembodiments. In the embodiments, one or more electrode leads may be usedfor stimulation of one or more neural structures within one or bothhemispheres of the brain, each lead comprising one or more electrodeslocated near the tip of each lead. Each of the electrodes may be usedfor stimulation, monitoring, or both stimulation and monitoring. One ormore of these electrodes may be implanted. Implanted electrodes may beused independently or in addition to one or more electrodes placed onthe outside of the brain or skull.

A typical DBS electrode lead tip 70, such as that incorporated into theMedtronic® DBS Lead Model 3387, is shown in FIG. 8. The lead tip 70comprises a first electrode 72 a, a second electrode 72 b, a thirdelectrode 72 c, and a fourth electrode 72 d. Once implanted into thebrain, each of the electrodes 72 a, 72 b, 72 c, 72 d may be used toapply a stimulus to one or more neural structures or monitor andoptionally record the evoked response (including ERNA) from neuralcircuits to the stimulus. In other embodiments, leads with moreelectrodes or electrodes with different sizes or topologies may be used.In addition, one or more reference electrodes may be located at a remotesite and used to complete the electrical circuit when one or moreelectrodes on the DBS lead are activated for stimulation or used forsignal monitoring.

The target location for the lead tip 70 varies dependent on the neuralstructure. Example target structures include but are not limited to thesubthalamic nucleus (STN), the substantia nigra pars reticulata (SNr),and the globus pallidus interna (GPi).

FIG. 9 shows the lead tip 70 implanted into a brain at a targetstructure, in this case the subthalamic nucleus (STN) 82. It will beappreciated that intersecting the electrode tip 70 with the subthalamicnucleus (STN) 82, which has a typical diameter of 5 to 6 mm, can be avery difficult surgical task. Techniques such as stereotactic imaging,microelectrode recordings, intraoperative x-ray imaging, and applyingtherapeutic stimulation whilst monitoring patient symptoms, arecurrently used to localise the electrode tip 70. However, these methodscan lack accuracy. Additionally, existing methods usually require thepatient to be awake for the procedure, since voluntary responses fromthe patient can be used to confirm that the electrode is at a suitablelocation relative to a target structure in the brain. For this reason,many potential recipients of DBS therapy turn down the option becausethey are not comfortable with having to be awake during the surgicalprocedure.

The accuracy of locating electrodes of the electrode tip 70 within atarget structure can be greatly increased by using a series of patternedstimulations to generate and measure an evoked resonant response from aneural target. Such techniques can obviate the need for the patient tobe awake during the implantation procedure, since an electrode can belocated much more accurately at the correct location within the brainand relative to a target neural structure. This means that patients canbe under sedation or general anaesthetic during the surgery since nopatient feedback is required to locate the electrode to a satisfactorydegree of accuracy.

An example DBS delivery system 90 according to an embodiment of thepresent disclosure is illustrated in FIG. 10. The system 90 comprisesthe lead tip 70 of FIG. 8 including the plurality of integratedelectrodes 72 a, 72 b, 72 c, 72 d, together with a processing unit 92, asignal generator 94, a measurement circuit 96 and an optionalmultiplexer 98. The processing unit comprises a central processing unit(CPU) 100, memory 102, and an input/output (I/O) bus 104 communicativelycoupled with one or more of the CPU 100 and memory 102.

In some embodiments, the multiplexer 98 is provided to control whetherthe electrodes 72 a, 72 b, 72 c, 72 d are connected to the signalgenerator 94 and/or to the measurement circuit 96. In other embodimentsthe multiplexer may not be required. For example, the electrodes 72 a,72 b, 72 c, 72 d may instead be connected directly to both the signalgenerator 94 and the measurement circuit 96. Although in FIG. 10 all ofthe electrodes 72 a, 72 b, 72 c, 72 d are connected to the multiplexer98, in other embodiments, only one or some of the electrodes 72 a, 72 b,72 c, 72 d may be connected.

The measurement circuit 96 may include one or more amplifiers anddigital signal processing circuitry including but not limited tosampling circuits for measuring neural responses to stimulation,including ERNA. In some embodiments the measurement circuit 96 may alsobe configured to extract other information from received signals,including local field potentials. The measurement circuit 96 may also beused in conjunction with the signal generator 94 to measure electrodeimpedances. The measurement circuit 96 may be external to or integratedwithin the processing unit 92. Communication between the measurementcircuit 96 and/or the signal generator 94 on the one hand and the I/Oport on the other may be wired or may be via a wireless link, such asover inductive coupling, WiFi®, Bluetooth® or the like. Power may besupplied to the system 90 via at least one power source 106. The powersource 106 may comprise a battery such that elements of the system 90can maintain power when implanted into a patient.

The signal generator 94 is coupled via the multiplexer 98 to one or moreof the electrodes 72 a, 72 b, 72 c, 72 d and is operable to deliverelectrical stimuli to respective electrodes based on signals receivedfrom the processing unit 92. To this end, the signal generator 94, themultiplexer 98 and the processing unit 92 are also communicativelycoupled such that information can be transferred therebetween. Whilstthe signal generator 94, multiplexer 98, and the processing unit 92 inFIG. 10 are shown as separate units, in other embodiments the signalgenerator 94 and multiplexer may be integrated into the processing unit92. Furthermore, either unit may be implanted or located outside thepatient's body.

The system 90 may further comprise one or more input devices 108 and oneor more output devices 110. Input devices 108 may include but are notlimited to one or more of a keyboard, mouse, touchpad and touchscreen.Examples of output devices include displays, touchscreens, lightindicators (LEDs), sound generators and haptic generators. Input and/oroutput devices 108, 110 may be configured to provide feedback (e.g.visual, auditory or haptic feedback) to a user related, for example, tocharacteristics of ERNA or subsequently derived indicators (such asproximity of the electrode 70 relative to neural structures in thebrain. To this end, one or more of the input devices 108 may also be anoutput device 110, e.g. a touchscreen or haptic joystick. Input andoutput devices 108, 110 may also be wired or wirelessly connected to theprocessing unit 92. Input and output devices 108, 110 may be configuredto provide the patient with control of the device (i.e. a patientcontroller) or to allow clinicians to program stimulation settings, andreceive feedback of the effects of stimulation parameters on ERNAcharacteristics.

One or more elements of the system 90 may be portable. One or moreelements may be implantable into the patient. In some embodiments, forexample, the signal generator 94 and lead 70 may be implantable into thepatient and the processing unit 92 may be external to the patient's skinand may be configured for wireless communication with the signalgenerator via RF transmission (e.g. induction, Bluetooth®, etc.). Inother embodiments, the processing unit 92, signal generator 94 and lead70 may all be implanted within the patient's body. In any case, thesignal generator 94 and/or the processing unit 92 may be configured towirelessly communicate with a controller (not shown) located external tothe patient's body.

One embodiment of the present disclosure provides a system and methodfor localising the lead tip 70 within a target structure of the brainusing measured ERNA. During an operation for implantation of the leadtip 70 into the brain, instead of relying on low accuracy positioningtechniques as described above to estimate the location of electrodesrelative to neural structures within the brain, the system 90 may beused to provide real-time feedback to the surgeon based oncharacteristics such as the strength and quality of evoked responsesignals received from one or more electrodes of the lead tip 70. Thisfeedback may be used to estimate position within the target structure inthree dimensions and to inform the decision of whether to reposition theelectrodes or remove and reimplant the electrodes along a differenttrajectory.

FIG. 11 shows a general example of such a process. The process begins atstep 112 with an electrode lead tip such as that described withreference to FIG. 8 being advanced during surgery towards a targetneural structure along a predefined trajectory. The step size (orspatial resolution) by which the electrode lead is advanced may bechosen by the surgeons and/or clinicians. In some embodiments, the stepsize is 1 mm. At step 114, evoked responses, including ERNA, aremeasured by applying a patterned stimulus such as those described aboveto an electrode of the lead tip 70. The stimulus may be applied for thewhole time whilst the lead tip 70 is being implanted. Alternatively, thepatterned signal may be repeated a predetermined number of times, suchas 10 times. The evoked response may be measured at the same electrodeas that used to apply the stimulus or may be measured at one or moredifferent electrodes. By doing so, a more accurate estimate of thelocation of each electrode relative to the target neural structure maybe provided. Steps 112 and 114 are repeated until the electrode lead tiphas been inserted to the maximum allowable depth, which may be in thetarget neural structure or slightly beyond it.

By repeating steps 112 and 114, a profile or map of evoked responses atdifferent locations along the insertion trajectory may be generated. Theprofile of evoked responses may include measurements from multipleelectrodes or from just one electrode. The profile of evoked responsesat different depths may be output to the one or more output devices 110.The profile of evoked responses are then compared at step 118 in orderto determine whether a preferred electrode location can be identified.The identification of preferred electrode location may be based ondifferent ERNA features, including relative differences between orspatial derivatives of amplitude, rate of decay, rate of change, andfrequency, at different insertion positions (e.g. the location thatproduces the largest resonances).

The identification of a preferred electrode location may also be basedon comparison with template ERNA activity, where the templates have beenderived from recordings from other patients. The profile of evokedresponses may also be used to estimate the trajectory of the electrodelead 70 through the target neural structure, including the boundaries ofthe structure and the region intersected (e.g. the trajectory passedthrough the medial or lateral region). The profile of evoked responsesmay also be used to estimate the proximity to the target structure, inthe event that the target structure is not intersected by the insertiontrajectory.

If at step 120 a preferred electrode location can be identified, theelectrode lead tip 70 can be repositioned at step 122, such that anelectrode is positioned at the preferred location. Alternatively, forembodiments that include electrode lead tips with a large number ofelectrodes, the electrode positioned closest to the preferred locationcan be nominated for subsequent use in applying therapeutic stimulation.If at step 120 a preferred location cannot be identified, the surgeonand/or clinician may choose to remove the electrode and re-implant alonga different trajectory.

Another embodiment of the present disclosure provides a system andmethod for determining the relative positions of an array of electrodeswith respect to a target neural structure and then selecting a preferredelectrode to use for applying therapeutic stimulation. This processcould be performed during electrode implantation surgery to assist inthe positioning of electrodes, or with previously implanted electrodeswhen programming the device to deliver therapeutic stimulation. Astimulus may be applied at more than one of the electrodes of the array,for example two or more of electrodes 72 a, 72 b, 72 c, 72 d in the caseof electrode array 70. Where a patterned stimulation regime is used,sequential bursts of a stimulus pattern may be applied to different onesof the electrodes 72 a, 72 b, 72 c, 72 d. Alternatively, a full stimuluspattern may be applied at one electrode, followed by another fullstimulus pattern at another electrode. By doing so, a determination maybe made concerning which electrode of an electrode array is positionedbest to provide therapeutic stimulation to one or more of the targetneural structures; for example, which of the electrodes 72 a, 72 b, 72c, 72 d is best positioned within a target neural structure.

FIG. 12 illustrates an example process 130 for measuring evoked responsefrom an array of multiple electrodes. At step 132, a stimulus is appliedto the first electrode of an array of X electrodes (electrode 72 a inthe case of the lead tip 70). The stimulus applied may be a burstpatterned stimulus as described above. The evoked response from a targetneural structure is measured at one or more of the electrodes in thearray at step 134. In the case of the lead tip 70, for example, theevoked response may be measured at the second, third and fourthelectrodes 72 b, 72 c, 72 d when the first electrode 72 a is beingstimulated. In some embodiments, the evoked responses received at thestimulating electrode may also be recorded and optionally stored inmemory. Once the evoked response has been measured at each electrode,another electrode is selected for stimulation. This may be achieved byincrementing a counter, as shown in step 138, after the process haschecked at step 136 to see whether all electrodes in the array of Xelectrodes have been stimulated, i.e. whether the process has cycledthrough all of the electrodes in the system. If there are electrodesremaining to be stimulated, then the process repeats, applying astimulus to the next selected electrode in the array. If all electrodesin the array have been stimulated and an evoked response to stimulationat each electrode measured and recorded, the resultant measured evokedresponses are then processed at step 140.

Processing the evoked responses may involve comparing different ERNAfeatures, including relative differences between or spatial derivativesof amplitude, rate of decay, rate of change, and frequency, acrossdifferent combinations of electrodes used for stimulation andmeasurement. For example, the processing may involve. identifying theelectrode that measures the largest evoked resonance amplitude for eachstimulation condition). The identification of the preferred electrodelocation may also be based on a comparison with template ERNA activity.Templates may be derived from recordings from other patients or from oneor more models or simulations.

Based on the processing of the evoked responses, a preferred electrodeto use for therapeutic stimulation may be chosen at step 142. Theresults of the ERNA processing and a recommendation for the preferredelectrode may be output to the one or more output devices 110. If theprocess has been performed during surgery, the results of the ERNAprocessing may also be used to determine which electrodes are within thetarget neural structure and whether to reposition the electrode array.The results may also be used to generate one or more templates forfuture processing of evoked responses in the same or different patients.

FIG. 13 shows an example of the evoked responses measured at each of thefirst, second and fourth electrodes 72 a, 72 b, 72 d based on patternedstimulation applied to the third electrode 72 c. This examplecorresponds to one iteration of steps 132 and 134 of process 130 shownin FIG. 12. The stimulated electrode 72 c is represented with thecrossed axes. Firstly, it is shown that a resonant response over severalcycles can be measured using the novel patterned stimulus. Secondly, itcan be seen that the response at the second electrode 72 b has thelargest amplitude, the amplitude of response at the fourth electrode 72d has the smallest amplitude, and the amplitude of the evoked responseat the first electrode 72 a is substantially less than that at thesecond electrode 72 b but slightly greater than that at the fourthelectrode 72 d. These results indicate that the second electrode 72 b isclosest to or within the target neural structure and the first andfourth electrodes 72 a, 72 d are outside of the target neural structure.

Whilst in the above example the evoked response is measured at threeelectrodes, in other embodiments, the evoked response may be measured atone or two or any number of electrodes in any configuration. Forexample, ERNA could be measured and/or recorded from differentcombinations of electrodes. Additionally or alternatively, measurementelectrodes may implanted in and/or positioned external to the brain orskull.

FIG. 14 graphically illustrates example evoked responses to stimulationin accordance with the process 130 of FIG. 12 applied to the lead tip 70of the system 90 shown in FIG. 10. Each column of graphs represents oneof four stimulation conditions with the stimulated electrode representedby the crossed panel, i.e. each column is an iteration of steps 132 and134 of process 130. The data shown in FIG. 14 was measured with thesecond and third electrodes 72 c, 72 d positioned within the subthalamicnucleus (STN) of a patient's brain. It can be seen that the largestevoked responses are observed at each of the second and third electrodes72 b, 72 c when the other of those electrodes 72 c, 72 b is stimulated.Accordingly, by comparing the measured evoked responses at eachelectrode in response to stimulation at another electrode, adetermination can be made firstly of whether any of the electrodes arepositioned within the target neural structure, secondly whether any ofthe electrodes are positioned at an optimum location within the targetneural structure, and thirdly the direction and/or distance of aparticular electrode from that target neural structure. In someembodiments, one or more of the presence, amplitude, natural frequency,damping, rate of change, envelope, and fine structure of a evokedresonant response to a stimulus may be used to identify the mosteffective electrode in an electrode array. Additionally, it can be seenthat the evoked responses vary depending on the position of theelectrode used for stimulation, illustrating the feasibility of usingthe process illustrated in FIG. 11 to localise electrodes within atarget neural structure.

The process 130 of FIG. 12 may be repeated using different stimulationparameters (e.g. using different stimulation amplitudes or frequencies)or with more than one stimulating electrode in step 132 (e.g.stimulation applied concurrently through multiple electrodes on one ormore electrode leads). The response characteristics obtained may be usedto aid current steering (e.g. setting the distribution of currentsacross electrodes that are active simultaneously) and the selection ofactive electrodes (e.g. which electrodes to use for stimulation). Forexample, response characteristics can be used to estimate the spatialspread of activation relative to the target area. Using thisinformation, the stimulation profile may be shaped using two or moreelectrodes to direct stimulation to particular areas of the brain, i.e.towards a target structure, and away from areas which the clinician doesnot wish to stimulate.

In a further embodiment, ERNA can also be used to optimize stimulationparameters used to target various medical conditions. For instance, oncean electrode array such as the lead tip 70 has been accurately locatedwithin a target neural structure, the setting of stimulation parametersfor therapeutic DBS can be aided by measuring ERNA, improving accuracyand time- and cost-efficiency, and reducing undesirable side-effects.

The change in elicited resonant activity for different stimulationparameters may be used to optimize stimulation settings. Such processescan enable therapy to be tailored to the individual needs of patientsand can be performed with minimal clinical intervention. In someembodiments, one or more of the presence, amplitude, natural frequency,damping, rate of change, envelope, and fine structure of an evokedresonant response to a stimulus may be used to optimise stimulation.Such response characteristics may be used to adjust amplitude,frequency, pulse width, and shape of a stimulation waveform.

A parameter of therapeutic stimulation that is particularly difficult toset using state of the art techniques is stimulation frequency. This ispartly because optimum stimulation frequency can vary from patient topatient; typically between around 90 Hz to around 185 Hz. In embodimentsof the present disclosure, one or more of the above describedcharacteristics of ERNA may be used to set frequency of stimulation(e.g. the time period t₂ between pulses in a burst). For example, thestimulation frequency might be selected to approximate a multiple orsubmultiple of a frequency component of the ERNA, such as the estimatedfundamental frequency of the ERNA.

It will be appreciated that some or all of the parameters listed abovemay have synergistic or adverse effects on one another and thus theeffectiveness of treatment. Accordingly, in some embodiments, knownoptimisation techniques such as machine learning or particle swarm maybe implemented to find an optimal set of parameter values within themultidimensional parameter space. Such techniques may involve aniterative process of trying a selection of different parameter settingsto determine the most effective parameter values based on the monitoredERNA.

To further optimise therapeutic DBS, the above techniques for ERNAmonitoring and DBS parameter optimisation can be performed on a patientbefore and after administration of medication for relieving symptoms ofa condition. For example, a record of ERNA for a particular patient whois on or off such medication may be used as a benchmark for an evokedresonant response which provides the most benefit to a patient so thatparameters can be tuned to try to replicate such evoked response states.FIG. 15 schematically illustrates a method of determining stimulationparameters based on the ERNA responsive to the stimulation of amedicated patient. At step 144, a stimulus is applied to an implantedelectrode in a target neural structure of a patient before beingadministered with any medication, and the ERNA from the stimulus ismeasured and recorded at step 146. The patient is then medicated at step148. For example, a clinician may administer a dose of a drug (e.g.,levodopa) to the patient. At steps 150 and 152 the process ofstimulation and measurement of resonant response are repeated. The ERNAbefore and after the medicament is administered is then used todetermine stimulation parameters which approximate those of thepatient's medicated state. In particular, DBS parameter settings may bechosen which, when administered, replicate or approximate the transitionfrom the uncontrolled-symptom ERNA to the controlled-symptom ERNA.

In some embodiments, optimisation processes may be performed by aclinician when the system 90 is being installed or during a visit to ahealthcare centre. Additionally or alternatively, the optimisation maybe run by the patient or may be instigated by the system 90automatically. For example, the system 90 may implement an optimisationprocess periodically (e.g. every day, week or month). In otherembodiments, an optimisation process could be initiated on replacementor recharge of a battery, in circumstances where the power source 106includes a battery. Other conditions that could trigger an optimisationprocess include a change in the patient's state, such as whether thepatient is engaged in a fine motor task, a gross motor task, speaking,sleeping, or is sedentary.

In some embodiments, the system 90 may store a series of previouslyoptimised settings in the memory 102. These stored settings maycorrespond to the optimised settings for different patient states (e.g.fine or gross motor activation, sleeping or sedentary) and may includestimulation being applied to different target neural structures. Thepatient may be given the ability to choose which of the storedstimulation settings they want to use at any given time, through the useof a patient controller. Alternatively, the system 90 may automaticallychoose which of the stored stimulation settings to use based onmeasurements of the patients state from electrophysiological signals(e.g. ERNA or local field potentials) recorded from the electrodes 70 bysystem 90 or from measurements taken with input devices 108 of system 90(e.g. accelerometers).

In addition to enhancing the accuracy of locating a DBS electrode in thebrain, choosing electrode configurations for stimulation and optimisingstimulation parameters, ERNA may be used to generate feedback forcontrolling the stimulation of electrodes. In some embodiments, feedbackmay be implemented using the system 90 shown in FIG. 10.

In one embodiment, the system 90 may use a waveform templatecorresponding to a preferred patient state. The template may begenerated using previous recordings of ERNA in a patient with reducedsymptoms. For example, ERNA templates recorded from a medicated patientor a patient receiving effective stimulation treatment may be used.Alternatively, ERNA templates recorded from a healthy patient, e.g. apatient without a movement disorder, may be used. Templates may beconstructed from the average of many recordings from one patient orseveral patients. In some embodiments, selected features of the ERNAwaveform may be used instead of a complete template. For example,parameters of the ERNA such as the dominant frequency and amplitudecomponents and/or temporal features may be used to enable improvedelectrode placement and control of therapeutic stimulation. In someembodiments, preferred ranges for different ERNA characteristics may bedefined (e.g. stimulation is controlled such that the ERNA frequencyremains within 250-270 Hz).

Referring to FIG. 10 and FIG. 16, the processing unit 92 may sendinstructions/signals to the signal generator 94 to generate a patternedstimulation signal which may or may not have been pre-calibrated inaccordance with an embodiment described above. The signal generator 94may then generate the signal at step 160 and apply it to one of theelectrodes 72 a, 72 b, 72 c, 72 d of the lead tip 70 (step 162). Theprocessing unit 92 may then measure the ERNA and monitor one or moreparameters (or characteristics) of the ERNA (step 164). The processingunit 92 may then process the received ERNA data (step 166). In someembodiments, the processing unit 92 may compare the ERNA (or one moreparameters thereof) with a resonant response associated with effectivetherapy (or one or more parameters thereof). Based on the ERNA data, theprocessing unit 92 may then instruct the signal generator to adjust oneor more parameters of the stimulation signal applied to one of theelectrodes 72 a, 72 b, 72 c, 72 d (step 168).

In some embodiments, bursts of stimulation, such as those describedabove, in combination with the monitoring of ERNA may be used toidentify a therapeutic resonant state (e.g. a state which correlateswith good symptom suppression with minimal side effects and/or minimumelectrical power consumption). From this information, therapeuticstimulation parameters required to produce the preferred therapeuticstate may be identified. In some embodiments, these stimulationparameters may be used to apply continuous therapeutic DBS to the targetneural structure.

Probe bursts for identifying resonant activity can be interleaved withthe therapeutic DBS to re-assess the resonant state. These probe burstsmay be implemented on a periodic basis, for example, every 10 seconds.In one embodiment, every 10 seconds, a probe burst may be applied for 1second (e.g. 10 pulses at 130 Hz) and the ERNA assessed. The therapeuticstimulation parameters may then be adjusted or maintained based on theERNA. For example, if there is a change in ERNA relative to the lastprobe burst, the stimulation parameters may be adjusted such that theERNA becomes comparable with the previously measured ERNA and/or thetemplate ERNA and/or an ERNA characteristic is within a desired range.

There are a number of ways in which the therapeutic stimulation may beadjusted based on the measured ERNA. In some embodiments, if theresonant circuit is in a preferred resonant state, e.g. if the measuredERNA substantially matches a template or if an ERNA characteristic iswithin a desired range, the amplitude of the therapeutic stimulation maybe reduced by the signal generator 94 in response to an instruction fromthe processing unit 92. Conversely, if the neural circuit is not in apreferred resonant state, the amplitude of therapeutic stimulation maybe increased by the signal generator 94.

In some embodiments, if a therapeutic resonance is detected, the DBSstimulation may be switched off altogether or until after the next probeburst is applied to generate a measurable ERNA. Then when the next probeburst is applied, if the resonance is no longer therapeutic, the DBSstimulation may be switched back on.

In some embodiments, a comparison of multiple resonant components in asingle measured evoked response may be used as a measure of stimulationefficacy and may be used as a control variable to control stimulationparameters.

In some embodiments, the length of continuous stimulation blocks(between probe bursts) and the duration of the probe bursts may beadjusted to optimise the ERNA. Longer continuous stimulation periods orblocks between probe bursts will reduce the computation load on theprocessing unit 92 and thus increase power efficiency but may alsoresult in greater variation of ERNA from the preferred ERNA and thus areduction in effectiveness of treatment.

There is an inherent requirement for implanted and portable DBS devicesto provide the best treatment of symptoms while minimising both sideeffects and power consumption. In one embodiment, a method for operatingthe system 90 using closed-loop feedback is provided in which the dutycycle of stimulation is modulated with an aim to minimise stimulationon-time. FIG. 17 illustrates a process which may be performed by thesystem 90. At step 170 a stimulation signal is generated. Parameters ofthe stimulation signal are chosen so as to optimise the ERNA to apreferred resonant state. The stimulus is then applied to an electrodeof the lead tip 70 for a period T at step 172. The period T may be afixed period. Preferably the stimulus is applied continuously orperiodically until a preferred state of ERNA is reached. The therapeuticstimulation is then stopped and the evoked response is measured at oneor more electrodes, the evoked response being to a probe stimuluscomprising one or more bursts of pulses applied to the stimulationelectrode (at step 174). In some embodiments the probe stimulus may beapplied to more than one electrode. In some embodiments, the stimulationelectrode can be used to measure ERNA instead of or in addition to theone or more other electrodes. The system is then maintained in thisstate of monitoring until the ERNA becomes undesirable. In someembodiments, the determination of whether or not the ERNA is in apreferred or therapeutic state may be performed by comparing themeasured response with a template ERNA response or by comparing ameasured ERNA characteristic with a desired range. As soon as it isconsidered that the state is undesirable at step 176, a stimulationsignal is again generated and applied at steps 170 and 172.

FIG. 18 graphically compares a stimulation regime 178 comprising apatterned therapeutic stimulation signal 182 followed by anon-therapeutic patterned stimulation signal (comprising one or morebursts of pulses) 184 and a corresponding characteristic (e.g. resonantfrequency) 180 of the ERNA varying between a preferred state 186 and aless than preferable state 188.

There are several different ways of implementing the patterned signalsof embodiments described herein. FIGS. 19A and 19B illustrate twoexemplary patterning profiles. In FIG. 19A, the patterned profileincludes a period of no stimulation 192 after a continuous stimulationblock 190, followed by a burst of pulses 194 and another period of nostimulation 196. During the period of no stimulation, the ERNA may bemeasured and therapeutic stimulation signal adjusted (if required).

In an alternative embodiment, the system may monitor the ERNA after afinal pulse of continuous stimulation 198 as shown in FIG. 19B. After aperiod of monitoring 200, the therapeutic stimulation may then beadjusted for a period 202 after which the therapeutic stimulation 198may be applied with the adjusted parameters. This regime may also beconsidered as continuous stimulation with periodic missing pulses. Tothis end, the continuous stimulation may be considered as a burst ofpulses, and the period of no stimulation may be considered as the firsttime period t₁ as described with reference to FIG. 2 above.

In other embodiments, instead of omitting stimulation during themonitoring period 200, stimulation may be maintained but with alteredparameters, as described previously with reference to FIGS. 5A and 5B.For example, conventional therapeutic stimulation at a frequency of,e.g. 130 Hz, may be applied during the therapeutic stimulation period198. Then during the monitoring period 200, a stimulus having adifferent frequency, may be applied. The stimulus applied during themonitoring period may be lower than that applied during the stimulationperiod. For example, the stimulation frequency during this period may bein the region of 90 Hz. The frequency of this stimulus during themonitoring period 200 may be low enough to allow for multiple ERNA peaksto be observed. Equally, the frequency of the stimulus applied duringthe monitoring period 200 may be high enough to be within thetherapeutic frequency range for DBS. As mentioned above, the transitionbetween frequency may be abrupt or, alternatively the change infrequency may be gradual. Applying ramps to the frequency of the pulsesto avoid an abrupt step change in frequency may be advantageous.

Additionally or alternatively, the amplitude of the stimulus appliedduring the monitoring period 200 may differ to that applied during thetherapeutic period 198. For example, the amplitude of the stimulusapplied during the monitoring period 200 may be less than that appliedduring therapeutic stimulation 198. An amplitude ramp may be applied totransition the stimulus between the therapeutic period 198 and themonitoring period 200 over several pulses, to avoid an abrupt stepchange in amplitude.

In some embodiments, characteristics of the stimulus other thanamplitude and frequency may differ between the stimulation period 198and the monitoring period. Examples of such characteristics include, butare not limited to, frequency, amplitude, pulse-width, net charge,electrode configuration, or morphology of the stimulus.

The presence and amplitude of ERNA can be dependent on stimulationamplitude. Accordingly, so as to maintain consistency in ERNAmeasurements, it may be preferable to always use the same pulseparameter settings and in particular the same amplitude for the pulseused to measure ERNA. The last pulse before the period of no stimulationmay therefore be at a fixed amplitude which is independent of theamplitude of stimulation being applied by other pulses (e.g. therapeuticstimulation), so as to minimise any effect due to resonance dependenceon stimulation amplitude or other pulse parameters.

Whilst in embodiments described above, a single electrode array is usedboth to stimulate and record an evoked neural response, in otherembodiments, electrodes may be distributed on multiple probes or leadsin one or more target structures in either or both brain hemispheres.Equally, electrodes either implanted or positioned external to the brainmay be used to stimulate or record or both stimulate and record anevoked neural response. In some embodiments, a combination of bothmicroelectrode and macroelectrodes may be used in any foreseeablemanner.

In a further application of the embodiments of the present inventionERNA measurements may be recorded and tracked over time to monitor theprogression or remission of a disease or syndrome, or used as adiagnostic tool (e.g. to classify the patient's neurological condition).Such embodiments may also be used to provide medical alerts to thepatient, a caregiver or a clinician in the event that the patient'sstate (as determined by ERNA) deteriorates towards an undesirable orcritical state (e.g. a Parkinsonian crisis).

In yet another application, ERNA may be used to monitor the effects ofmedication over time, including the effects of adjusting medicationdoses, etc. Such an embodiment may also be used to provide medicationalerts to the patient to remind them when a dose is required or when adose has been skipped. Tracking medication effects with ERNA may alsoprovide clinicians with information regarding whether medication isbeing taken as prescribed or whether medication is becoming lesseffective and requires dosing adjustment.

Further Analysis of ERNA and Explanation of Results

DBS Evokes Resonant Neural Activity

The neural activity resulting from DBS pulses was investigated todetermine if there were evoked responses that could feasibly be used asa biomarker. To preserve evoked activity we used a wide recordingbandwidth, as well as symmetric biphasic pulses for stimulation, ratherthan conventional asymmetric pulses with a very long second phase, tominimize the temporal duration of stimulation artefacts.

Recordings were made from DBS electrodes immediately following theirimplantation in the STN of patients with PD who were still awake on theoperating table, as PD is the predominant application for DBS.Furthermore, the STN's roles in regulation of motor, limbic, andassociative function make it a neural target relevant to a number ofdifferent applications, including DBS treatment of dystonia, essentialtremor, epilepsy, and obsessive-compulsive disorder.

It was found that STN-DBS evokes a large peak typically around 4 msafter each pulse. By examining the activity following the last pulseprior to cessation of DBS it was discovered that this peak is the firstin a series with progressively decreasing amplitude. As this responsehas a form that resembles a decaying oscillation, we describe it asevoked resonant neural activity (ERNA).

To further investigate ERNA, we temporally patterned standard 130 Hz DBSto allow multiple peaks to be observed. We employed two novel patterns:skipping one pulse every second, and applying a burst of ten pulsesevery second. The ‘skipped-pulse’ pattern was anticipated to havecomparable therapeutic effects to standard 130 Hz DBS, as it causes onlya 0.77% reduction in the total number of pulses delivered over time. Incontrast, the ‘burst’ pattern was anticipated to have minimaltherapeutic effects relative to continuous DBS, as only 7.7% of thepulses are delivered, making it a useful probe for investigatingactivity in the absence of therapy. The evoked responses tend toincrease in amplitude and sharpen across the consecutive pulses of aburst, and reach a steady state for longer duration stimuli.

We applied the burst stimulus to the STN of 12 PD patients (n=23hemispheres) undergoing DBS implantation surgery and observed ERNA ofsimilar morphology in all cases, indicating it is a robust and reliablesignal that can be measured across the patient population. As a controlto ensure that ERNA was not a specious artefact, we also applied theburst stimulus to 3 Essential Tremor patients (n=6 hemispheres) withelectrodes implanted in the posterior subthalamic area (PSA), a whitematter region medial to the STN. ERNA was not observed in the PSA,suggesting it is an electrophysiological response localizable to theSTN.

ERNA is Localizable to the STN

To establish that ERNA varies with electrode position relative to theSTN, we consecutively applied 10 s of burst stimulation to each of thefour DBS electrodes whilst recording from the three unstimulatedelectrodes. The DBS implantation surgery aimed to position two of thefour electrodes within the STN, with one electrode in the dorsal STNwhere DBS usually has greatest benefit and another in the ventral STN.This variance in electrode location facilitated comparison of ERNAresponses from different regions of the STN with those outside of thenuclei. In 8 STN-PD patients (n=16 hemispheres), we found that both ERNAamplitude and morphology varied depending on the stimulating andrecording electrode positions. FIG. 14 shows exemplar ERNA from the lastpulse of each burst in one hemisphere, with the responses with thelargest amplitude and most apparent decaying oscillation morphologyoccurring at the two middle electrodes in the target STN position. As acontrol, we also applied stimulation to two Essential Tremor patients(n=4 hemispheres) using an implantation trajectory intended to positiondistal electrodes within the PSA and proximal electrodes in the ventralintermediate nucleus of the thalamus, another target for tremor that haspreviously been shown not to elicit evoked activity beyond 2 ms.

As variation in ERNA amplitude was the most apparent feature, we used itfor further analysis. As not all recordings were in STN and containeddistinct resonant activity, to quantify ERNA amplitude we calculated theroot mean square (RMS) voltage over 4-20 ms. To estimate implantedelectrode positions relative to the STN, 3D reconstructions (FIG. 22)were generated using post-operative CT scans merged with pre-operativeMRI (FIGS. 23A-23C). Electrodes were classified as being either superiorto, inferior to, or within the STN, based on blinded measurementsrelative to the red nucleus. Within-STN electrodes were then furtherclassified to be either dorsal or ventral.

ERNA amplitude varied significantly with electrode position(Kruskal-Wallis, H(4)=45.73, p<0.001), with only the inferior electrodesnot significantly different from the PSA region (p=0.370) (FIG. 24).Although dorsal electrodes tended to be higher than ventral andsuperior, they were not significantly different in amplitude from eachother. To account for disparity in amplitude across patients due tovariation in electrode positioning in the medial-lateral andposterior-anterior planes and underlying differences in patientphysiology, we re-analyzed recordings from the STN-DBS electrodes afternormalizing responses to the total ERNA amplitude across eachhemisphere. After normalization, post hoc comparisons revealed asignificant difference in amplitude between dorsal and ventral STN(Kruskal-Wallis, H(3)=14.94, p=0.002; Dunn's method post hoc, dorsal vsventral: p=0.043, dorsal vs superior: p=0.081, dorsal vs inferior:p=0.002).

These results show that ERNA is localizable to, and varies across, theSTN, establishing its utility as a feedback signal for guiding electrodeimplantation to the most beneficial sites for stimulation. Furthermore,whilst variation in amplitude was the most apparent feature, other ERNAproperties, such as frequency, latency, and rate of change, also havepotential utility in discriminating STN regions.

ERNA is Modulated by DBS

To investigate whether ERNA was modulated by therapeutically effectiveDBS, we applied skipped-pulse stimuli of progressively increasingcurrent amplitude (range 0.67-3.38 mA) in blocks of 60-90 s to 10 STN-PDpatients (n=19 hemispheres). In general, it was found that the secondand subsequent peaks in ERNA were consistently observed toasymptotically increase in latency and spread further apart over timeand as stimulation amplitudes increased, consistent with a decrease inthe frequency of the resonant activity (FIGS. 5A-5F, 6A-6F, 21A to 21E).In many cases, the latency of the first peak also increased, althoughthis change was not consistent across all recordings. The amplitude ofthe peaks was also generally observed to vary, often being greater atthe beginning of each stimulation block and then gradually diminishing.

To quantify these effects, we calculated the inverse of the differencein latency between the first and second peaks as a representativemeasure of ERNA frequency. We also calculated the amplitude differencebetween the first peak and the first trough as a representative measureof ERNA amplitude. We then used averages of the 45-60 s period of eachcondition as estimates of asymptotic ERNA values for analysis.

ERNA frequency significantly decreased across conditions (1-way RepeatedMeasures (RM) ANOVA, F(4,94)=45.79, p<0.001). Post hoc comparisons(Holm-Sidak) showed that ERNA frequency significantly decreased witheach increasing step of DBS amplitude (FIG. 25A), except from 2.25 mA to3.38 mA (p=0.074). The median frequency was 256 Hz at 3.38 mA,approximately two times the stimulation rate of 130 Hz. It has beenproposed that STN-DBS could act by creating a pacing effect within theglobus pallidus interna at two times the stimulation rate, due to theimmediate excitation of STN axons and inhibition/recovery time course ofSTN soma.

ERNA amplitude was also significantly different across conditions(Friedman, x²(4)=41.31, p<0.001). Tukey test post hoc comparisonsindicated that ERNA amplitude initially increased with DBS amplitude andthen plateaued at levels above 1.5 mA (FIG. 25B). While these effectsmay be related to the therapeutic effects of DBS, they may alternativelyor additionally be due to saturation of neural firing in the STN. Due tothis, we subsequently focused on ERNA frequency for correlation withtherapeutic effects.

ERNA Correlates with Therapeutic Effects

The clinical efficacy of stimulation was confirmed by rating limbbradykinesia and rigidity according to the Unified Parkinson's DiseaseRating Scale (UPDRS; items 22 and 23) immediately prior to stimulationand after 60 s at 2.25 mA. Both clinical signs improved significantly at2.25 mA indicating that DBS was therapeutic (Wilcoxon Signed Rank,bradykinesia: Z=−3.62, p<0.001, rigidity: Z=−3.70, p<0.001).

However, time constraints precluded clinical examinations with each stepin stimulation intensity. Therefore, to correlate ERNA modulation withpatient state, we used beta-band (13-30 Hz) spontaneous LFP activity.Excessive synchronization of oscillations within the beta band has beenstrongly implicated in the pathophysiology of PD and its suppression hasbeen correlated with improvement in movement impairments of bradykinesiaand rigidity.

Using short-time Fourier transforms we calculated ‘relative beta’, theRMS amplitude within the 13-30 Hz band divided by that within 5-45 Hz,as a representative measure of beta activity. We then averaged acrossthe 45-60 s period of each stimulation amplitude condition and foundrelative beta to significantly vary (1-way RM ANOVA, F(4,89)=18.11,p<0.001). Post hoc tests (Holm-Sidak) showed significant suppression at3.38 mA compared to all other conditions and at 2.25 mA compared to 0.67and 1 mA (FIG. 25C). These results are consistent with previous studiesshowing that beta activity suppression by DBS correlates withimprovement in clinical signs, and confirm that stimulation wastherapeutically effective at and above 2.25 mA.

To further correlate ERNA frequency with beta activity, and thustherapeutic efficacy, we compared average values for 15 snon-overlapping blocks across each condition (FIG. 25B). ERNA frequencywas significantly correlated with relative beta (Pearson product moment,ρ=0.601, n=90, p<0.001).

These results indicate that ERNA is a clinically relevant biomarker.Furthermore, its large amplitude, ranging from 20 μVp-p to 681 μVp-p(median 146 μVp-p), is orders of magnitude larger than spontaneous betaLFP activity, whose absolute values ranged from 0.9 to 12.5 μVRMS(median 2.2 μVRMS). The robustness of ERNA and its distinct andgradually modulated morphology (FIGS. 5, 6, 7, 21) contrast with theinherent variability in beta-band activity across patients and noisybursting on-off nature of the beta-band signal. ERNA is therefore a moretractable signal to use with a fully-implantable DBS device compared tonoisy, low-amplitude LFP measures.

ERNA Modulation Washes Out Following DBS

Immediately before and after the therapeutic skipped-pulse stimulationwe also applied 60 s of burst stimulation (hereafter referred to as pre-and post-DBS conditions), in order to monitor changes in activity astherapeutic effects washed out.

Generally, ERNA remained relatively stable pre-DBS, indicating themodulatory effects of burst stimulation were minimal. However,immediately post-DBS, ERNA peaks occurred at longer latencies beforegradually returning towards their pre-DBS state. To quantify theseeffects, we averaged post-DBS ERNA frequency and amplitude over 15 snon-overlapping blocks and compared them to the last 15 s pre-DBS. Overall hemispheres tested (n=19), differences were found in ERNA frequency(Friedman, χ²(4)=70.23, p<0.001), with frequencies at all time pointssignificantly reduced compared to the pre-DBS frequency except for thefinal 45-60 s block (Tukey, p=0.73) (FIG. 26A). ERNA amplitude alsosignificantly varied across the time points (Friedman, χ²(4)=31.37,p<0.001), with differences between amplitudes at post-DBS time pointsindicating a washout of amplitude suppression caused by therapeuticstimulation (FIG. 26B). As the burst stimulus applied was constantacross the pre- and post-DBS conditions, the variation observed in ERNAfrequency and amplitude can be directly attributed to changes in thestate of STN neural circuits as DBS effects washed out. Therapeuticallyeffective DBS therefore modulates both ERNA frequency and amplitude,indicating ERNA has multiple properties that can feasibly be used asbiomarkers and as tools for probing mechanisms of action.

We then assessed relative beta activity pre- and post-DBS and found itto be significantly different across time points (Friedman, χ²(4)=24.55,p<0.001). Consistent with previous reports, relative beta wassignificantly decreased immediately post-DBS and washed out to pre-DBSlevels after 30 s (FIG. 26C). Supporting the skipped-pulse results, ERNAfrequency was significantly correlated with relative beta pre- andpost-DBS (Pearson product moment, ρ=0.407, n=152, p<0.001). ERNAamplitude was also correlated with relative beta (Pearson productmoment, ρ=0.373, n=152, p<0.001), suggesting it too may have clinicaland mechanistic relevance.

We also analyzed spontaneous LFP activity in the high frequencyoscillation (HFO) band (200-400 Hz), which overlaps with the observedERNA frequencies. Changes in the HFO band have been correlated withmotor state and effective pharmacological therapy, particularly inconjunction with beta activity, and have been implicated in themechanisms of action of DBS. Concurrent ERNA and HFO analysis wasenabled by the use of burst stimulation, as data could be segmented toonly include activity between bursts, thereby providing LFP epochs thatwere free of stimulation artefacts that can otherwise corrupt the HFOband.

As the HFO activity was generally characterized by a broadband peak infrequency, we calculated multitaper spectral estimates and thendetermined the frequency and amplitude of the peak occurring between200-400 Hz. Comparing averages across 15 s non-overlapping blocks (FIG.26D), we found HFO peak frequency to be significantly decreased post-DBS(Friedman, χ²(4)=45.18, p<0.001), until the final 45-60 s block (Tukey,p=0.077). This washout trend matches that for ERNA frequency, albeit ata lower frequency, with a significant correlation between the two(Pearson product moment, ρ=0.546, n=152, p<0.001). The median HFO peakfrequency immediately post-DBS was 253 Hz, comparable to the median ERNAfrequency of the therapeutic 3.38 mA condition (256 Hz), suggesting HFOactivity occurs at the same frequency as ERNA during the more continuousskipped-pulse stimulation.

No significant differences were found in HFO peak amplitude (Friedman,χ²(4)=2.11, p=0.72), although it did significantly correlate with ERNAamplitude (Pearson product moment, ρ=0.429, n=152). It is likely thatthe very small amplitude (<1 μV) of HFO peaks resulted in any modulatoryeffects being obscured by noise in the recordings.

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the above-describedembodiments, without departing from the broad general scope of thepresent disclosure. The present embodiments are, therefore, to beconsidered in all respects as illustrative and not restrictive.

We claim:
 1. A method of monitoring neural activity responsive to astimulus in a brain of a patient, the method comprising: a. applying thestimulus to one or more of at least one electrode implanted in or near atarget neural structure of the brain; b. detecting a resonant responsefrom the target neural structure evoked by the stimulus at one or moreof the at least one electrode in or near the target neural structure ofthe brain, the resonant response having a frequency of between 200 Hzand 500 Hz; and c. determining one or more waveform characteristics ofthe detected resonant response.
 2. The method of claim 1, wherein theone or more waveform characteristics are determined based on at leastpart of a second or subsequent cycle in the detected resonant response.3. The method of claim 1, wherein the one or more waveformcharacteristics comprises one or more of the following: a) a frequencyof the resonant response; b) a temporal envelope of the resonantresponse; c) an amplitude of the resonant response; d) a fine structureof the resonant response; e) a rate of decay of the resonant response;f) a delay between the onset of the stimulus and the onset of a temporalfeature of the resonant response; g) a rate of change of the resonantresponse.
 4. The method of claim 1, further comprising: adapting thestimulus based on the one or more determined waveform characteristics ofthe resonant response.
 5. The method of claim 4, wherein the stimulus isadapted based on the time elapsed between adjacent peaks in the resonantresponse, wherein the stimulus is adapted if the time elapsed betweentwo adjacent peaks in the resonant response is less than a firstpredetermined time period associated with alleviation of movementsymptoms in the patient, wherein the predetermined time periodassociated with alleviation of movement symptoms in the patient is lessthan about 3.5 ms.
 6. The method of claim 4, wherein the stimulus isadapted based on the time between the stimulus and one or more peaks ofthe resonant response, wherein the stimulus is adapted if the timeelapsed between the stimulus and a peak in the resonant response isbetween first and second predetermined time periods associated withalleviation of movement symptoms in the patient.
 7. The method of claim4, further comprising: correlating the detected resonant response with atemplate resonant response; and adapting the stimulus based on thecorrelation.
 8. The method of claim 1, wherein the stimulus comprises apatterned signal comprising a plurality of bursts separated by a firsttime period, each burst comprising a plurality of pulses separated by asecond time period, wherein the first time period is greater than thesecond time period and wherein the detecting is performed during one ormore of the first time periods.
 9. The method of claim 1, furthercomprising: applying a second stimulus to a target neural structure inthe brain; detecting a second resonant response from the target neuralstructure evoked by the second stimulus at one or more of the at leastone electrode implanted in or near the target neural structure;determining one or more second waveform characteristics of the detectedsecond resonant response.
 10. The method of claim 1, further comprising:determining whether one or more of the at least one electrode ispositioned in the target neural network based on the detected resonantresponse.
 11. The method of claim 10, wherein the at least one electrodecomprises a plurality of electrodes, and wherein the steps of applyingthe stimulus, detecting a resonant response and determining one or morewaveform characteristics of the detected resonant response are repeatedusing different combinations of the at least one electrode to generate aprofile of resonant responses.
 12. The method of claim 1, furthercomprising: repeating the steps of applying the stimulus, detecting aresonant response and determining one or more waveform characteristicsof the detected resonant response.
 13. The method of claim 1, furthercomprising: selecting one or more of the at least one electrode to usefor therapeutic stimulation of the target neural structure based on theone or more waveform characteristics; and applying a therapeuticstimulus to the target neural structure via the selected one or more ofthe at least one electrode.
 14. The method of claim 1, wherein the oneor more of the at least one electrode used to apply the stimuluscomprises at least two electrodes and/or wherein the one or more of theat least one electrode used to detect the resonant response comprises atleast two electrodes.
 15. The method of claim 1, wherein the neuraltarget structure is part of the cortico-basal ganglia-thalamocorticalcircuit.
 16. The method of claim 1, wherein the neural target structureis the subthalamic nucleus, globus pallidus interna, substantia nigrapars reticulata, pedunculopontine nucleus.
 17. The system of claim 16,wherein the one or more waveform characteristics are determined based onat least part of a second or subsequent cycle in the detected resonantresponse.
 18. The system of claim 17, wherein the stimulus is adaptedbased on the time elapsed between adjacent peaks in the resonantresponse, wherein the stimulus is adapted if the time elapsed betweentwo adjacent peaks in the resonant response is less than a firstpredetermined time period associated with alleviation of movementsymptoms in the patient, wherein the predetermined time periodassociated with alleviation of movement symptoms in the patient is lessthan about 3.5 ms.
 19. The system of claim 17, wherein the stimulus isadapted based on the time between the stimulus and one or more peaks ofthe resonant response, wherein the stimulus is adapted if the timeelapsed between the stimulus and a peak in the resonant response isbetween first and second predetermined time periods associated withalleviation of movement symptoms in the patient.
 20. The system of claim17, wherein the stimulus comprises a patterned signal comprising aplurality of bursts separated by a first time period, each burstcomprising a plurality of pulses separated by a second time period,wherein the first time period is greater than the second time period andwherein the detecting is performed during one or more of the first timeperiods.
 21. The system of claim 16, wherein the one or more waveformcharacteristics comprises one or more of the following: a) a frequencyof the resonant response; b) a temporal envelope of the resonantresponse; c) an amplitude of the resonant response; d) a fine structureof the resonant response; e) a rate of decay of the resonant response;f) a delay between the onset of the stimulus and the onset of a temporalfeature of the resonant response; g) a rate of change of the resonantresponse.
 22. The system of claim 16, wherein the processing unit isconfigured to: control the signal generator to adapt the stimulus basedon the one or more determined waveform characteristics of the resonantresponse.
 23. The system of claim 16, wherein the processing unit isconfigured to: estimate a degree of progression of a disease associatedwith the patient or an effect of a therapy provided to the patient basedon the one or more waveform characteristics and one or more secondwaveform characteristics, the one or more second waveformcharacteristics determined based on a second resonant response detectedafter the resonant response.
 24. The method of claim 1, wherein thepatient is under general anaesthetic.
 25. The method of claim 1, whereindetecting a resonart response from the target neural structure evoked bythe stimulus comprises detecting the resonant response at two or more ofthe at least one electrode; wherein the method further comprisescomparing the one or more waveform characteristics of the detectedresonant response detected at the two or more of the at least oneelectrode.
 26. The method of claim 25, further comprising adapting thestimulus based on the comparison.
 27. A neurostimulation system,comprising: a lead having at least one electrode adapted forimplantation in or near a target neural structure in a brain of apatient; a signal generator selectively coupled to one or more of the atleast one electrode and configured to generate a stimulus to stimulatethe target neural structure; a measurement device selectively coupled toone or more of the at least one electrode and configured to detect aresonant response from the target neural structure evoked by thestimulus, the resonant response having a frequency of between 200 Hz and500 Hz; a processing unit coupled to the measurement device andconfigured to determine one or more waveform characteristics of thedetected resonant response.
 28. The system of claim 27, whereindetecting a resonant response from the target neural structure evoked bythe stimulus comprises detecting the resonant response at two or more ofthe at least one electrode; wherein the method further comprises:comparing the one or more waveform characteristics of the detectedresonant response detected at the two or more of the at least oneelectrode; and adapting the stimulus based on the comparison.